Lane boundary detection

A method for lane boundary detection. The method may include obtaining an image of an environment of a vehicle. The environment may include at least one lane boundary; the images may include image segments; calculating image segment lane boundary metadata for relevant image segments. The calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein image segment lane boundary metadata that is related to a relevant image segment which may include (i) location information about location of the one or more lane boundary points candidates of the relevant image segment, and (ii) angular information regarding an angle between lane boundary points candidates of the image; and determining an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing.

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Description
BACKGROUND

In contrary to various road signs and vehicles—lane boundaries may have virtually arbitrary shape and orientation.

The virtually arbitrary shape dramatically increases the complexity of detecting lane boundaries. Thus—lane detection requires significant computational resources and is also very time consuming.

There is a growing need to provide a simple and reliable method for lane boundary detection.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciated more fully from the following detailed description, taken in conjunction with the drawings in which:

FIG. 1 is an example of a method;

FIG. 2 is an example of a vehicle and a system; and

FIG. 3 illustrates an example of an image and metadata.

 DESCRIPTION OF EXAMPLE EMBODIMENTS

In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention.

The subject matter regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of the specification. The invention, however, both as to organization and method of operation, together with objects, features, and advantages thereof, may best be understood by reference to the following detailed description when read with the accompanying drawings.

It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, the dimensions of some of the elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

Because the illustrated embodiments of the present invention may for the most part, be implemented using electronic components and circuits known to those skilled in the art, details will not be explained in any greater extent than that considered necessary as illustrated above, for the understanding and appreciation of the underlying concepts of the present invention and in order not to obfuscate or distract from the teachings of the present invention.

Any reference in the specification to a method should be applied mutatis mutandis to a device or computerized system capable of executing the method and/or to a non-transitory computer readable medium that stores instructions for executing the method.

Any reference in the specification to a computerized system or device should be applied mutatis mutandis to a method that may be executed by the computerized system, and/or may be applied mutatis mutandis to non-transitory computer readable medium that stores instructions executable by the computerized system.

Any reference in the specification to a non-transitory computer readable medium should be applied mutatis mutandis to a device or computerized system capable of executing instructions stored in the non-transitory computer readable medium and/or may be applied mutatis mutandis to a method for executing the instructions.

Any combination of any module or unit listed in any of the figures, any part of the specification and/or any claims may be provided.

The specification and/or drawings may refer to an image. An image is an example of a media unit. Any reference to an image may be applied mutatis mutandis to a media unit. A media unit may be an example of sensed information unit. Any reference to a media unit may be applied mutatis mutandis to sensed information. The sensed information may be sensed by any type of sensors—such as a visual light camera, or a sensor that may sense infrared, radar imagery, ultrasound, electro-optics, radiography, LIDAR (light detection and ranging), etc.

The specification and/or drawings may refer to a processor. The processor may be a processing circuitry. The processing circuitry may be implemented as a central processing unit (CPU), and/or one or more other integrated circuits such as application-specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), full-custom integrated circuits, etc., or a combination of such integrated circuits.

Any combination of any steps of any method illustrated in the specification and/or drawings may be provided.

Any combination of any subject matter of any of claims may be provided.

Any combinations of computerized systems, units, components, processors, sensors, illustrated in the specification and/or drawings may be provided.

Any reference to any of the term “comprising” may be applied mutatis mutandis to the terms “consisting” and “consisting essentially of”.

Any reference to any of the term “consisting” may be applied mutatis mutandis to the terms “comprising” and “consisting essentially of”.

Any reference to any of the term “consisting essentially of” may be applied mutatis mutandis to the terms “comprising” and “comprising”.

A lane boundary may be a road boundary or may differ from a road boundary.

There may be provided a system, a method and a non-transitory computer readable medium for lane boundary detection.

The method may include using an efficient neural network. The efficiency means that the neural network may be relatively shallow and/or uses limited computational resources.

The efficiency of the neural network may be contributed to one of more out of the following (at least some of the following are optional):

    • Processing an image that has fewer pixels than a sensed image of an environment of the vehicle.
    • Allocating the task of finding lane boundary point candidates to the neural network and allocating the task of determining an estimate of at least road boundary, based on the lane boundary point candidates, to a processor that does not apply machine learning calculations.
    • Training the neural network using supervised training that involves providing desired output values of location information and angular information of the lane boundary point candidates.
    • Outputting from the neural network, during the training process and during inference, quantized angular information.
    • Generating image segment based metadata—which reduces the feature map size required for detecting lane boundaries.
    • Training the neural network to a specific task with specific desired output values—which is much simpler than a general image segmentation process.

FIG. 1 illustrates method 100 for lane boundary detection.

Method 100 may start by initialization step 110.

Initialization step 110 may include receiving a neural network that was already trained or training the neural network.

Step 110 may be followed by step 120 of obtaining an image of an environment of a vehicle, the environment may include at least one lane boundary. The image may include image segments. The image segments may be defined regardless of the content of the image. The image segments may have shapes and sizes that are determined in advance. The image segments should be determined in a manner that does not impose extensive usage of computational resources.

The image may represent a sensed image and has more pixels than the image. Step 120 may include receiving the image, receiving the sensed image and converting the sensed image to the image—for example by compressing, quantizing, down-sampling, or performing any other pixel reduction process.

Step 120 may be followed by step 130 of calculating image segment lane boundary metadata for relevant image segments. A relevant image segment is “relevant” in the sense that it includes one or more lane boundary points candidates.

The calculating is executed by a neural network and is image-segment based.

An image segment lane boundary metadata that is related to a relevant image segment may include location information and angular information.

The location information is indicative of a location of the one or more lane boundary points candidates of the relevant image segment.

The angular information is indicative of an angle between lane boundary points candidates (LBPCs) of the image. This may be a direction from one lane boundary point candidate (LPPC) to the next LBPC.

The angular information related to a LBPC may be a quantized representation of an angle between the LBPC and another LBPC. The other LBPC may be the next and/or closest LBPC.

Using quantized information during the training process of the neural network—allows using a more compact neural network. When using the same neural network in the training and inference phases—the neural network used in step 120 may be compact.

The angular information related to the LBPC has up to four, five, six, seven or more quantized values. For example—allowing an angular range of about sixty degrees to be represented by a single quantized value.

Alternatively—the LBPC is not quantized.

It should be noted that different angular ranges of the LBPC may be calculated by different parts of the neural network—and that different nodes (of one or more layers) may be allocated for outputting values within different angular ranges. This allocation may reduce the energy consumed by the neural network.

The allocation may be learnt, for example, by using a training process that may induce different parts to be responsible to different angular ranges. For example—a cost function may induce one part to output angular information of a certain angular range—and suppress another part from outputting angular information of that certain angular range.

Step 130 may also include calculating a confidence metadata that is indicative of a level of confidence that the LBPC is a lane boundary point. The confidence metadata may be used in step 130 and may be a part of the angular information related to a LBPC.

Step 130 may be followed by step 140 of determining an estimate of a lane boundary of the at least one lane boundary. The outcome of step 140 may be referred to as lane boundary information.

The determining may be based on the image segment lane boundary metadata of the relevant image segments.

Step 140 may include applying a process that differs from neural network processing.

Step 140 may be followed by step 150 of responding to the determining.

The responding may include at least one out of:

    • Detecting one or more lanes delimited between lane boundaries.
    • Displaying at least one lane boundary or any other information about the at least one lane boundary to a user.
    • Generating and/or storing and/or transmitting lane boundary information.
    • Using lane boundary information to augment a display of the environment of the vehicle to a user.
    • Sending the lane boundary information to another processor.
    • Sending the lane boundary information to another sensor.
    • Fusing the lane boundary information with sensed data from another sensor.
    • Controlling a driving of the vehicle.
    • Providing the lane boundary information and/or suggested driving path to a human driver or an autonomous or semi-autonomous module of a vehicle.

Referring to step 110—step 110 may include training the neural network by a supervised learning process that includes providing to the neural network (a) training images that may include lane boundaries, and (b) desired output values of location information and angular information of at least some lane boundary points of the lane boundaries.

FIG. 2 illustrates a system 200 of vehicle 201.

System 200 may include visual sensor 220 such as a camera (illustrated as outputting sensed image 308), controller 230, processing unit 240 (that may include a neural network processor 242 and a non-NN processor 244), man machine interface such as display 250, memory unit 260, autonomous driving module 270 and ADAS module 280.

The NN processor 242 may perform NN calculations. NN calculations include performing NN by nodes of a NN.

The non-NN processor 244 may perform calculations that differ from NN processing.

The autonomous driving module 270, and ADAS module 280 may respond to lane boundary information generated by the processing unit 240.

The controller 230 may control the operation of the system 200.

System 200 may be configured to execute method 100.

FIG. 3 illustrates image 310 of a road having two lanes 321 and 322, and three land boundaries—leftmost lane boundary 331 (also a left road boundary), middle lane boundary 322 and rightmost lane boundary 333 (also a road boundary).

In FIG. 3 there are twenty relevant image segments 312—each includes at least some lane boundary points candidates. The LBPCs are collectively denoted 240.

FIG. 3 also illustrates an expanded view of a relevant image segment 312 that includes first LBPC 341 and second LBPC 342. The second LBPC 342 is followed by third LBPC 341 of another relevant image segment. Location information of each one of the first, second and third LBPC may be provided, as well as angular information regarding the angle of a virtual line between a first LBPC and a second LBPC, and the angle of a virtual line between a second LBPC and a third LBPC.

FIG. 3 also illustrates metadata 350 that includes (assuming twenty relevant image segments) twenty instances of image segment lane boundary metadata 351(1)-351(200)—one per relevant image segment.

Each instance of image segment lane boundary metadata includes location information and angular information—for example location information 350(20,1) and angular information 350(20,2) of image segment lane boundary metadata 351(20).

Metadata 350 may also include confidence level information 354—which may relate to the image segment lane boundary candidates and/or to the estimated lane boundaries.

While the foregoing written description of the invention enables one of ordinary skill to make and use what is considered presently to be the best mode thereof, those of ordinary skill will understand and appreciate the existence of variations, combinations, and equivalents of the specific embodiment, method, and examples herein. The invention should therefore not be limited by the above described embodiment, method, and examples, but by all embodiments and methods within the scope and spirit of the invention as claimed.

In the foregoing specification, the invention has been described with reference to specific examples of embodiments of the invention. It will, however, be evident that various modifications and changes may be made therein without departing from the broader spirit and scope of the invention as set forth in the appended claims.

Moreover, the terms “front,” “back,” “top,” “bottom,” “over,” “under” and the like in the description and in the claims, if any, are used for descriptive purposes and not necessarily for describing permanent relative positions. It is understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in other orientations than those illustrated or otherwise described herein.

Furthermore, the terms “assert” or “set” and “negate” (or “deassert” or “clear”) are used herein when referring to the rendering of a signal, status bit, or similar apparatus into its logically true or logically false state, respectively. If the logically true state is a logic level one, the logically false state is a logic level zero. And if the logically true state is a logic level zero, the logically false state is a logic level one.

Those skilled in the art will recognize that the boundaries between logic blocks are merely illustrative and that alternative embodiments may merge logic blocks or circuit elements or impose an alternate decomposition of functionality upon various logic blocks or circuit elements. Thus, it is to be understood that the architectures depicted herein are merely exemplary, and that in fact many other architectures may be implemented which achieve the same functionality.

Any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality may be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality.

Furthermore, those skilled in the art will recognize that boundaries between the above described operations merely illustrative. The multiple operations may be combined into a single operation, a single operation may be distributed in additional operations and operations may be executed at least partially overlapping in time. Moreover, alternative embodiments may include multiple instances of a particular operation, and the order of operations may be altered in various other embodiments.

Also for example, in one embodiment, the illustrated examples may be implemented as circuitry located on a single integrated circuit or within the same device. Alternatively, the examples may be implemented as any number of separate integrated circuits or separate devices interconnected with each other in a suitable manner.

However, other modifications, variations and alternatives are also possible. The specifications and drawings are, accordingly, to be regarded in an illustrative rather than in a restrictive sense.

In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word ‘comprising’ does not exclude the presence of other elements or steps then those listed in a claim. Furthermore, the terms “a” or “an,” as used herein, are defined as one or more than one. Also, the use of introductory phrases such as “at least one” and “one or more” in the claims should not be construed to imply that the introduction of another claim element by the indefinite articles “a” or “an” limits any particular claim containing such introduced claim element to inventions containing only one such element, even when the same claim includes the introductory phrases “one or more” or “at least one” and indefinite articles such as “a” or “an.” The same holds true for the use of definite articles. Unless stated otherwise, terms such as “first” and “second” are used to arbitrarily distinguish between the elements such terms describe. Thus, these terms are not necessarily intended to indicate temporal or other prioritization of such elements. The mere fact that certain measures are recited in mutually different claims does not indicate that a combination of these measures cannot be used to advantage.

While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will now occur to those of ordinary skill in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

It is appreciated that various features of the embodiments of the disclosure which are, for clarity, described in the contexts of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the embodiments of the disclosure which are, for brevity, described in the context of a single embodiment may also be provided separately or in any suitable sub-combination.

It will be appreciated by persons skilled in the art that the embodiments of the disclosure are not limited by what has been particularly shown and described hereinabove. Rather the scope of the embodiments of the disclosure is defined by the appended claims and equivalents thereof.

Claims

1. A method for lane boundary detection, the method comprises: obtaining an image of an environment of a vehicle, the environment comprises at least one lane boundary; the image comprises image segments;

calculating image segment lane boundary metadata for relevant image segments, wherein the calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein the calculating comprises calculating location information about location of the one or more lane boundary points candidates of each relevant image segment, and calculating, by different parts of the neural network each across a different angular range, angular information regarding an angle between lane boundary points candidates of the image; and
determining an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing;
wherein the neural network is a shallow neural network having limited computational resources.

2. The method according to claim 1 wherein the image represents a sensed image and has more pixels than the image.

3. The method according to claim 1 comprising obtaining a sensed image and converting the sensed image to the image, wherein the image has fewer pixels than the sensed image.

4. The method according to claim 1 wherein angular information related to a lane boundary point candidate is a quantized representation of an angle between the lane boundary point candidate and another lane boundary point candidate.

5. The method according to claim 1 wherein angular information related to a lane boundary point candidate also comprises a confidence metadata that is indicative of a level of confidence that the lane boundary point candidate is a lane boundary point.

6. The method according to claim 1 wherein the relevant image segments have a rectangular shape.

7. The method according to claim 1 comprising training the neural network by a supervised learning process that includes providing to the neural network (a) training images that comprise lane boundaries, and (b) desired output values of location information and angular information of at least some lane boundary points of the lane boundaries.

8. The method according to claim 1 comprising determining an estimate of each of the at least one lane boundary.

9. The method according to claim 1 comprising calculating a confidence level for the estimate of the lane boundary.

10. The method according to claim 1, wherein the neural network is trained by using a training process that induces the different parts of the neural network to be responsible to the different angular ranges.

11. The method according to claim 1, wherein the neural network is trained by applying a cost function that induces one part of the neural network to output angular information of a certain angular range and suppress another part of the neural network from outputting angular information of that certain angular range.

12. The method according to claim 4 wherein the angular information related to the lane boundary point candidate has up to seven quantized values.

13. A non-transitory computer readable medium that stores instructions for:

obtaining an image of an environment of a vehicle, the environment comprises at least one lane boundary; the image comprises image segments;
calculating image segment lane boundary metadata for relevant image segments, wherein the calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein the calculating comprises calculating location information about location of the one or more lane boundary points candidates of each relevant image segment, and calculating, by different parts of the neural network each across a different angular range, angular information regarding an angle between lane boundary points candidates of the image; and
determining an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing;
wherein the neural network is a shallow neural network having limited computational resources.

14. A computerized system comprising a memory unit and a processing unit, wherein the processing unit is configured to:

obtain an image of an environment of a vehicle, the environment comprises at least one lane boundary; the image comprises image segments;
calculate image segment lane boundary metadata for relevant image segments, wherein the calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein the calculating comprises calculating location information about location of the one or more lane boundary points candidates of each relevant image segment, and calculating, by different parts of the neural network each across a different angular range, angular information regarding an angle between lane boundary points candidates of the image and
determine an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing;
wherein the neural network is a shallow neural network having limited computational resources.
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Patent History
Patent number: 12423994
Type: Grant
Filed: Jul 1, 2022
Date of Patent: Sep 23, 2025
Assignee: AUTOBRAINS TECHNOLOGIES LTD (Tel Aviv)
Inventors: Roi Saida (Acco), Shay Ieizerovitch (Mikhmoret), Igal Raichelgauz (Tel Aviv)
Primary Examiner: Wednel Cadeau
Application Number: 17/810,577
Classifications
International Classification: G06V 20/56 (20220101); G06V 10/44 (20220101); G06V 10/82 (20220101);